Discovery of a Highly Promising Disulfide Derivative Scaffold as Inhibitor of SARS-CoV-2 Main Protease.

The main protease (Mpro) of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) represents a promising target for antiviral drugs aimed at combating COVID-19. Consequently, the development of Mpro inhibitor is an ideal strategy for combating the virus. In this study, we identified twenty-two dithiocarbamates (1 a-h), dithiocarbamate-Cu(II) complexes (2 a-hCu) and disulfide derivatives (2 a-e, 2 i) as potent inhibitors of Mpro, with IC50 value range of 0.09-0.72, 0.9-24.7, and 15.1-111 µM, respectively, through FRET screening. The enzyme kinetics, inhibition mode, jump dilution, and DTT assay revealed that 1 g may be a partial reversible inhibitor, while 2 d and 2 f-Cu are the irreversible and dose- and time-dependent inhibitors, potentially covalently binding to the target. Binding of 2 d, 2 f-Cu, and 1 g to Mpro was found to decrease the stability of the protein. Additionally, DTT assays and thermal shift assays indicated that 2 f-Cu and 2 d are the nonspecific and promiscuous cysteine protease inhibitor. ICP-MS implied that the inhibitory activity of 2 f-Cu may stem from the uptake of Cu(II) by the enzyme. Cytotoxicity assays demonstrated that 2 d and 1 g exhibit low cytotoxicity, whereas 2 f-Cu show certain cytotoxicity in L929 cells. Overall, this work presents two promising scaffolds for the development of Mpro inhibitors to combat COVID-19.

Deep learning for mining protein data.

The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.

Dihydroxyphenyl-substituted thiosemicarbazone: A potent scaffold for the development of metallo-ß-lactamases inhibitors and antimicrobial.

The superbug infection mediated by metallo-ß-lactamases (MßLs) has grown into anemergent health threat, and development of MßL inhibitors is an ideal strategy to combat the infection. In this work, twenty-five thiosemicarbazones 1a-e, 2a-e, 3a-e, 4a-d, 5a-d and 6a-b were synthesized and assayed against MßLs ImiS, NDM-1 and L1. The gained molecules specifically inhibited NDM-1 and ImiS, exhibiting an IC50 value in the range of 0.37-21.35 and 0.45-8.76 µM, and 2a was found to be the best inhibitor, with an IC50 of 0.37 and 0.45 µM, respectively, using meropenem (MER) as substrate. Enzyme kinetics and dialysis tests revealed and confirmed by ITC that 2a is a time-and dose-dependent inhibitor of ImiS and NDM-1, it competitively and reversibly inhibited ImiS with a Ki value of 0.29 µM, but irreversibly inhibited NDM-1. Structure-activity relationship disclosed that the substitute dihydroxylbenzene significantly enhanced inhibitory activity of thiosemicarbazones on ImiS and NDM-1. Most importantly, 1a-e, 2a-e and 3a-b alone more strongly sterilized E. coli-ImiS and E. coli-NDM-1 than the MER, displaying a MIC value in the range of 8-128 µg/mL, and 2a was found to be the best reagent with a MIC of 8 and 32 µg/mL. Also, 2a alone strongly sterilized the clinical isolates EC01, EC06-EC08, EC24 and K. pneumonia-KPC-NDM, showing a MIC value in the range of 16-128 µg/mL, and exhibited synergistic inhibition with MER on these bacteria tested, resulting in 8-32-fold reduction in MIC of MER. SEM images shown that the bacteria E. coli-ImiS, E. coli-NDM-1, EC24, K. pneumonia-KPC and K. pneumonia-KPC-NDM treated with 2a (64 µg/mL) suffered from distortion, emerging adhesion between individual cells and crumpled membranes. Mice tests shown that monotherapy of 2a evidently limited growth of EC24 cells, and in combination with MER, it significantly reduced the bacterial load in liver and spleen. Docking studies suggest that the 2,4-dihydroxylbenzene of 2a acts as zinc-binding group with the Zn(II) and the residual amino acids in CphA active center, tightly anchoring the inhibitor at active site. This work offered a promising scaffold for the development of MßLs inhibitors, specifically the antimicrobial for clinically drug-resistant isolates.

pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP.

BACKGROUND: Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP. RESULTS: Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2-5. CONCLUSIONS: pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align.

DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network.

MOTIVATION: Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem. RESULTS: This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units' models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction. AVAILABILITY AND IMPLEMENTATION: The method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods.

Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.

Comparison of different fractionation strategies for in-depth phosphoproteomics by liquid chromatography tandem mass spectrometry.

Phosphorylation, a major posttranslational modification of proteins, plays an important role in protein activity and cell signaling. However, it is difficult to detect protein phosphorylation because of its low abundance and the fact that the analysis can be hindered by the presence of highly abundant non-phosphoproteins. In order to reduce the sample complexity and improve the efficiency of identification of phosphopeptides, aliphatic hydroxy acid-modified metal oxide chromatography (HAMMOC) was utilized to enrich phosphopeptides from a murine macrophage cell lysate. Strong cation chromatography (SCX), electrostatic repulsion hydrophilic interaction chromatography (ERLIC), and solution isoelectric focusing (sIEF) were investigated in detail for phosphopeptide fractionation strategies followed by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. A total of 5744 non-redundant phosphopeptides and 2159 phosphoproteins were identified from the cell lysates in three fractionation approaches. The SCX fractionation contained the largest number of phosphoproteins and phosphopeptides that were identified. In addition, 4336, 2064, and 2424 phosphopeptides were identified from SCX-LC-MS/MS, ERLIC-LC-MS/MS, and sIEF-LC/MS-MS, including 2430, 438, and 751 phosphopeptides that were only specifically found in SCX, ERLIC, and sIEF fractionations. In conclusion, these three fractionation strategies demonstrated great complementarity, which greatly improved the efficiency of identification of phosphopeptides and can be suitable for use in in-depth phosphoproteome research. Graphical Abstract.

Discovery of hydroxamate as a promising scaffold dually inhibiting metallo- and serine-ß-lactamases.

The bacterial infection mediated by ß-lactamases MßLs and SßLs has grown into an emergent health threat, however, development of a molecule that dual inhibits both MßLs and SßLs is challenging. In this work, a series of hydroxamates 1a-g, 2a-e, 3a-c, 4a-c were synthesized, characterized by 1H and 13C NMR and confirmed by HRMS. Biochemical assays revealed that these molecules dually inhibited MßLs (NDM-1, IMP-1) and SßLs (KPC-2, OXA-48), with an IC50 value in the range of 0.64-41.08 and 1.01-41.91 µM (except 1a and 1d on SßLs, IC50 > 50 µM), and 1f was found to be the best inhibitor with an IC50 value in the range of 0.64-1.32 and 0.57-1.01 µM, respectively. Mechanism evaluation indicated that 1f noncompetitively and irreversibly inhibited NDM-1 and KPC-2, with Ki value of 2.5 and 0.55 µM, is a time- and dose-dependent inhibitor of both MßLs and SßLs. MIC tests shown that all hydroxamates increased the antimicrobial effect of MER on E. coli-NDM-1 and E. coli-IMP-1 (expect 1b, 1d, 1g and 2d), resulting in a 2-8-fold reduction in MICs of MER, 1e-g, 2b-d, 3a-c and 4b-c decreased 2-4-fold MICs of MER on E. coli-KPC-2, and 1c, 1f-g, 2a-c, 3b, 4a and 4c decreased 2-16-fold MICs of MER on E. coli-OXA-48. Most importantly, 1f-g, 2b-c, 3b and 4c exhibited the dual synergizing inhibition against both E. coli-MßLs and E. coli-SßLs tested, resulting in a 2-8-fold reduction in MICs of MER, and 1f was found to have the best effect on the drug-resistant bacteria tested. Also, 1f shown synergizing antimicrobial effect on five clinical isolates EC04, EC06, EC08, EC10 and EC24 that produce NDM-1, resulting in a 2-8-fold reduction in MIC of MER, but its effect on E. coli and K. pneumonia-KPC-NDM was not to be observed using the same dose of inhibitor. Mice tests shown that the monotherapy of 1f or 4a in combination with MER significantly reduced the bacterial load of E. coli-NDM-1 and E. coli-OXA-48 cells in liver and spleen, respectively. The discovery in this work offered a promising bifunctional scaffold for creating the specific molecules that dually inhibit MßLs and MßLs, in combating antibiotic-resistant bacteria.

Inhalable spray-dried porous microparticles containing dehydroandrographolide succinate phospholipid complex capable of improving and prolonging pulmonary anti-inflammatory efficacy in mice.

Due to the unique physiological barriers within the lungs, there are considerable challenges in developing drug delivery systems enabling prolonged drug exposure to respiratory epithelial cells. Here, we report a PulmoSphere-based dry powder technology that incorporates a drug-phospholipid complex to promote intracellular retention of dehydroandrographolide succinate (DAS) in respiratory epithelial cells following pulmonary delivery. The DAS-phospholipid complex has the ability to self-assemble into nanoparticles. After spray-drying to produce PulmoSphere microparticles loaded with the drug-phospholipid complex, the rehydrated microparticles discharge the phospholipid complex without altering its physicochemical properties. The microparticles containing the DAS-phospholipid complex exhibit remarkable aerodynamic properties with a fine particle fraction of ∼ 60% and a mass median aerodynamic diameter of ∼ 2.3 µm. These properties facilitate deposition in the alveolar region. In vitro cell culture and lung tissue explants experiments reveal that the drug-phospholipid complex prolongs intracellular residence time and lung tissue retention due to the slow intracellular disassociation of drug from the complex. Once deposited in the lungs, the DAS-phospholipid complex loaded microparticles increase and extend drug exposure to the lung tissues and the immune cells compared to the free DAS counterpart. The improved drug exposure to airway epithelial cells, but not immune cells, is related to a prolonged duration of pulmonary anti-inflammation at decreased doses in a mouse model of acute lung injury induced by lipopolysaccharide. Overall, the phospholipid complex loaded microparticles present a promising approach for improved treatment of respiratory diseases, e.g. pneumonia and acute respiratory distress syndrome.

Pulmonary delivery of size-transformable nanoparticles improves tumor accumulation and penetration for chemo-sonodynamic combination therapy.

Very little is currently known about how inhaled nanomedicine for lung cancer treatment overcomes biological barriers hampering the tumor availability of drug and nanoparticles. Here, we developed a size-transformable nanocarrier (~ 119 nm) in which small-size nanoparticles (~ 28 nm) were loaded in the large nanocarrier after the addition of modified hyaluronan and could be released upon size-transformation at tumor tissue. Subsequently, the pulmonary and tumor pharmacokinetics of the two nanocarriers containing 7-ethyl-10-hydroxycamptothecin (SN38) and a covalently linked fluorescent sonosensitizer were comparatively investigated after intratracheal instillation to mice bearing orthotopic Lewis lung carcinoma tumors. The results showed that both instilled nanoparticles seemed to transport drug to tumor by direct access and transcytosis of nanoparticles, and diffusion of the released drug with the latter accounting for a great proportion of the drug tumor bioavailability. Relative to the small-size nanocarrier, the size-transformable counterpart appeared to restrict the mucociliary and absorption clearances from the lung and the clearance from the tumor interstitium to circulation, leading to increases in lung and tumor bioavailability of SN38 by 58.5% and 199%, respectively. In addition, the size-transformable nanoformulation conferred deep tumor penetration and sustained levels of both sonosensitizer and SN38 within tumors and simultaneously exerted sonodynamic- and chemo-therapies. Overall, the pulmonary delivery of size-transformable nanocarrier could co-deliver sonosensitizer and drug to deep tumor sites with enhanced tumor accumulation to realize combination therapy in lung cancer.

Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China.

Estimation of construction waste generation (CWG) at the field scale is a crucial but challenging task for effective construction waste management (CWM). Extant field-scale CWG modeling approaches have faced difficulties in obtaining accurate results due to a lack of detailed CWG data, and most of them fail to consider the complex relationship among predictive variables. This study attempts to tackle this issue by proposing a novel CWG modeling approach that integrates improved on-site measurement (IOM) and a support vector machine (SVM)-based prediction model. To achieve this goal, 206 ongoing commercial construction sites were investigated to obtain the predictor values and waste generation rates (WGRs) of five types of waste (i.e., inorganic nonmetallic waste, organic waste, metal waste, composite waste, and hazardous waste) generated at three construction stages (i.e., the understructure stage, superstructure stage, and finishing stage). The data were introduced to the SVM to develop the relationships between predictive variables and WGRs. An actual commercial building under construction was used to demonstrate the applicability of the proposed approach. The results showed that the superiority of the IOM can be used as a basis to implement robust CWG data collection. In addition, the SVM-based WGR prediction model (SWPM) can obtain more accurate prediction results (R2 = 86.87%) than the back-propagation neural network (R2 = 75.14%) and multiple linear regression (R2 = 61.93%).

Using mixed methods to design service quality evaluation indicator system of railway container multimodal transport.

Multimodal transport can bring the technical and economic advantages in different transportation modes into full play. While ensuring the level of service, it can reduce energy consumption and transport costs. Governments of most countries are actively promoting it. Therefore, it has become a research hot spot. Being a green, fast, and all-day transport mode, railways play an important role in multimodal transport. This article aims to analyze a multimodal transport service quality indicator system involving railways from the perspectives of customers, multimodal service providers, and governments. Qualitative and quantitative research methods were adopted to analyze the secondhand data of academic papers, government policy, and industry reports to clarify the quality characteristics of multimodal transport services. Using grounded theory and to analyze firsthand data from in-depth interviews with multimodal transport practitioners, 25 evaluation indicators of container multimodal transport service quality were chosen to be the evaluation index system. To test and improve the evaluation scale, 270 valid questionnaires were analyzed using SPSS 24.0 and AMOS 21.0 software, including reliability analysis, exploratory factor analysis, and confirmatory factor analysis. The results show that all the indicators meet the standard requirements and have good reliability and validity.

Fluticasone propionate nanosuspensions for sustained nebulization delivery: An in vitro and in vivo evaluation.

This study intended to investigate the in vivo pulmonary fate of intratracheally dosed nanosuspensions of fluticasone propionate (FP). Three FP suspensions, including a microsuspension and two nanosuspensions with different dissolution profiles, were prepared and they exhibited comparable aerodynamic performances after nebulization via a jet nebulizer. Following intratracheal administration to rats, the microsuspension underwent extensive mucociliary clearance, leading to a limited absorption time whereas the nanosuspensions decreased the mucociliary clearance and allowed dissolution rate-limiting and extended pulmonary absorption, resulting in prolonged pulmonary retention and long-acting anti-inflammatory efficacy in a lipopolysaccharide induced lung injury model. Delaying the FP dissolution of a nanosuspension by phospholipid coating increased AUC value in lung tissues to 1.72-fold of a conventional nanosuspension, but led to a decreased pharmacological efficacy. This study demonstrated that inhalable nanosuspensions were a feasible means for the sustained pulmonary delivery of FP and the local anti-inflammatory efficacy was highly dependent on the dissolution profiles.

Intracellular hydrogelation preserves fluid and functional cell membrane interfaces for biological interactions.

Cell membranes are an intricate yet fragile interface that requires substrate support for stabilization. Upon cell death, disassembly of the cytoskeletal network deprives plasma membranes of mechanical support and leads to membrane rupture and disintegration. By assembling a network of synthetic hydrogel polymers inside the intracellular compartment using photo-activated crosslinking chemistry, we show that the fluid cell membrane can be preserved, resulting in intracellularly gelated cells with robust stability. Upon assessing several types of adherent and suspension cells over a range of hydrogel crosslinking densities, we validate retention of surface properties, membrane lipid fluidity, lipid order, and protein mobility on the gelated cells. Preservation of cell surface functions is further demonstrated with gelated antigen presenting cells, which engage with antigen-specific T lymphocytes and effectively promote cell expansion ex vivo and in vivo. The intracellular hydrogelation technique presents a versatile cell fixation approach adaptable for biomembrane studies and biomedical device construction.

An Automatic Classification Method on Chronic Venous Insufficiency Images.

Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.

[DNA repair of CHL cells and HeLa cells after DNA damage induced by different oxidative agents].

OBJECTIVE: To investigate DNA repair in CHL cells and HeLa cells after DNA damage induced by different oxidative agents. METHODS: CHL cells and HeLa cells were exposed to various damaging agents, CHL cells: H(2)O(2) for 25 min, K(2)Cr(2)O(7) for 105 min, doxorubicin (Dox) for 75 min HeLa cells: H(2)O(2) for 25 min, K(2)Cr(2)O(7) for 105 min; then cells were continuously cultured for 0-3 h after washing. Alkaline single cell gel electrophoresis (ASCGE) assay was used to detect DNA strand breaks. RESULT: (1) DNA strand breaks were induced in CHL cells after exposure to H(2)O(2) K(2)Cr(2)O(7) or Dox, which were repaired evidently after continuous culture for 1 h(P<0.01). The damages induced by H(2)O(2) or K(2)Cr(2)O(7) were repaired completely after culture for 2-3 h. However, the demage induced by Dox was repaired incompletely. (2) DNA strand breaks were induced also in HeLa cells after exposure to H(2)O(2) or K(2)Cr(2)O(7), which were repaired evidently after continuous culture for 0.5 h(P<0.01),and completely after culture for 1 h. (3) The regression coefficient related to the rate of comet cells and repair time was statistically different (P<0.05) between CHL cells and HeLa cells. CONCLUSION: DNA damage induced by Dox is repaired more difficult than that induced by H(2)O(2) or K(2)Cr(2)O(7). The repair initiates immediately after DNA damage in both of cells, but more rapidly in HeLa cells than in CHL cells.

Understanding attitudes towards proenvironmental travel: an empirical study from Tangshan City in China.

Understanding people's attitudes towards proenvironmental travel will help to encourage people to adopt proenvironmental travel behavior. Revealed preference theory assumes that the consumption preference of consumers can be revealed by their consumption behavior. In order to investigate the influences on citizens' travel decision and analyze the difficulties of promoting proenvironmental travel behavior in medium-sized cities in China, based on revealed preference theory, this paper uses the RP survey method and disaggregate model to analyze how individual characteristics, situational factors, and trip features influence the travel mode choice. The field investigation was conducted in Tangshan City to obtain the RP data. An MNL model was built to deal with the travel mode choice. SPSS software was used to calibrate the model parameters. The goodness-of-fit tests and the predicted outcome demonstrate the validation of the parameter setting. The results show that gender, occupation, trip purpose, and distance have an obvious influence on the travel mode choice. In particular, the male gender, high income, and business travel show a high correlation with carbon-intensive travel, while the female gender and a medium income scored higher in terms of proenvironmental travel modes, such as walking, cycling, and public transport.